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Nomogram for the Prediction of High-Grade Dysplasia and Invasive Carcinoma in Patients With Intraductal Papillary Mucinous Neoplasms of the Pancreas Based on Variables of Noninvasive Examination

Intraductal papillary mucinous neoplasms (IPMNs) are a heterogeneous group of neoplasms and represent the most common identifiable precursor lesions of pancreatic cancer. Clinical decision-making of the risk for malignant disease, including high-grade dysplasia and invasive carcinoma, is challenging...

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Autores principales: Li, Bo, Shi, Xiaohan, Gao, Suizhi, Shen, Shuo, Bian, Yun, Cao, Kai, Pan, Yaqi, Zhang, Guoxiao, Jiang, Hui, Li, Gang, Guo, Shiwei, Jin, Gang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7985057/
https://www.ncbi.nlm.nih.gov/pubmed/33767983
http://dx.doi.org/10.3389/fonc.2021.609187
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author Li, Bo
Shi, Xiaohan
Gao, Suizhi
Shen, Shuo
Bian, Yun
Cao, Kai
Pan, Yaqi
Zhang, Guoxiao
Jiang, Hui
Li, Gang
Guo, Shiwei
Jin, Gang
author_facet Li, Bo
Shi, Xiaohan
Gao, Suizhi
Shen, Shuo
Bian, Yun
Cao, Kai
Pan, Yaqi
Zhang, Guoxiao
Jiang, Hui
Li, Gang
Guo, Shiwei
Jin, Gang
author_sort Li, Bo
collection PubMed
description Intraductal papillary mucinous neoplasms (IPMNs) are a heterogeneous group of neoplasms and represent the most common identifiable precursor lesions of pancreatic cancer. Clinical decision-making of the risk for malignant disease, including high-grade dysplasia and invasive carcinoma, is challenging. Moreover, discordance on the indication for resection exists between the contemporary guidelines. Furthermore, most of the current nomogram models for predicting malignant disease depend on endoscopic ultrasonography to evaluate the precise size of mural nodules. Thus, this study aimed to propose a model to predict malignant disease using variables from a noninvasive examination. We evaluated patients who underwent resection of pathologically confirmed IPMNs between November 2010 and December 2018 and had preoperative clinical data available for review. Based on binary multivariable logistic regression analysis, we devised a nomogram model to predict malignant IPMNs. The area under the receiver operating characteristics curve (AUC) was used to evaluate the discrimination power of the model. Of the 333 patients who underwent resection of IPMNs, 198 (59.5%) had benign and 135 (40.5%) had malignant IPMNs. Multivariable logistic regression analysis showed that cyst size, cyst location, cyst wall enhancement, multicystic lesion, diameter of main pancreatic duct, neutrophil-to-lymphocyte ratio, serum carbohydrate antigen 19-9, and carcinoembryonic antigen were significantly associated with malignancy. The nomogram, constructed based on these variables, showed excellent discrimination power with an AUC of 0.859 (95% CI: 0.818–0.900, P < 0.001). In conclusion, we have developed a nomogram consisting of a combination of cross-sectional imaging features and blood markers, variables that can readily be obtained by noninvasive examinations during the surveillance period, which can distinguish benign from malignant IPMNs. Nevertheless, external validation is warranted.
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spelling pubmed-79850572021-03-24 Nomogram for the Prediction of High-Grade Dysplasia and Invasive Carcinoma in Patients With Intraductal Papillary Mucinous Neoplasms of the Pancreas Based on Variables of Noninvasive Examination Li, Bo Shi, Xiaohan Gao, Suizhi Shen, Shuo Bian, Yun Cao, Kai Pan, Yaqi Zhang, Guoxiao Jiang, Hui Li, Gang Guo, Shiwei Jin, Gang Front Oncol Oncology Intraductal papillary mucinous neoplasms (IPMNs) are a heterogeneous group of neoplasms and represent the most common identifiable precursor lesions of pancreatic cancer. Clinical decision-making of the risk for malignant disease, including high-grade dysplasia and invasive carcinoma, is challenging. Moreover, discordance on the indication for resection exists between the contemporary guidelines. Furthermore, most of the current nomogram models for predicting malignant disease depend on endoscopic ultrasonography to evaluate the precise size of mural nodules. Thus, this study aimed to propose a model to predict malignant disease using variables from a noninvasive examination. We evaluated patients who underwent resection of pathologically confirmed IPMNs between November 2010 and December 2018 and had preoperative clinical data available for review. Based on binary multivariable logistic regression analysis, we devised a nomogram model to predict malignant IPMNs. The area under the receiver operating characteristics curve (AUC) was used to evaluate the discrimination power of the model. Of the 333 patients who underwent resection of IPMNs, 198 (59.5%) had benign and 135 (40.5%) had malignant IPMNs. Multivariable logistic regression analysis showed that cyst size, cyst location, cyst wall enhancement, multicystic lesion, diameter of main pancreatic duct, neutrophil-to-lymphocyte ratio, serum carbohydrate antigen 19-9, and carcinoembryonic antigen were significantly associated with malignancy. The nomogram, constructed based on these variables, showed excellent discrimination power with an AUC of 0.859 (95% CI: 0.818–0.900, P < 0.001). In conclusion, we have developed a nomogram consisting of a combination of cross-sectional imaging features and blood markers, variables that can readily be obtained by noninvasive examinations during the surveillance period, which can distinguish benign from malignant IPMNs. Nevertheless, external validation is warranted. Frontiers Media S.A. 2021-03-09 /pmc/articles/PMC7985057/ /pubmed/33767983 http://dx.doi.org/10.3389/fonc.2021.609187 Text en Copyright © 2021 Li, Shi, Gao, Shen, Bian, Cao, Pan, Zhang, Jiang, Li, Guo and Jin http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
Li, Bo
Shi, Xiaohan
Gao, Suizhi
Shen, Shuo
Bian, Yun
Cao, Kai
Pan, Yaqi
Zhang, Guoxiao
Jiang, Hui
Li, Gang
Guo, Shiwei
Jin, Gang
Nomogram for the Prediction of High-Grade Dysplasia and Invasive Carcinoma in Patients With Intraductal Papillary Mucinous Neoplasms of the Pancreas Based on Variables of Noninvasive Examination
title Nomogram for the Prediction of High-Grade Dysplasia and Invasive Carcinoma in Patients With Intraductal Papillary Mucinous Neoplasms of the Pancreas Based on Variables of Noninvasive Examination
title_full Nomogram for the Prediction of High-Grade Dysplasia and Invasive Carcinoma in Patients With Intraductal Papillary Mucinous Neoplasms of the Pancreas Based on Variables of Noninvasive Examination
title_fullStr Nomogram for the Prediction of High-Grade Dysplasia and Invasive Carcinoma in Patients With Intraductal Papillary Mucinous Neoplasms of the Pancreas Based on Variables of Noninvasive Examination
title_full_unstemmed Nomogram for the Prediction of High-Grade Dysplasia and Invasive Carcinoma in Patients With Intraductal Papillary Mucinous Neoplasms of the Pancreas Based on Variables of Noninvasive Examination
title_short Nomogram for the Prediction of High-Grade Dysplasia and Invasive Carcinoma in Patients With Intraductal Papillary Mucinous Neoplasms of the Pancreas Based on Variables of Noninvasive Examination
title_sort nomogram for the prediction of high-grade dysplasia and invasive carcinoma in patients with intraductal papillary mucinous neoplasms of the pancreas based on variables of noninvasive examination
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7985057/
https://www.ncbi.nlm.nih.gov/pubmed/33767983
http://dx.doi.org/10.3389/fonc.2021.609187
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